Generate Simulation Data for Benchmarking Sparse Regressions (Gaussian Response)
Generate simulation data (Gaussian case) following the settings in Xiao and Xu (2015).
OHPL.sim(n = 100, p = 100, rho = 0.8, coef = rep(1, 10), snr = 3, p.train = 0.5, seed = 1001)
n |
Number of observations. |
p |
Number of variables. |
rho |
Correlation base for generating correlated variables. |
coef |
Vector of non-zero coefficients. |
snr |
Signal-to-noise ratio (SNR). |
p.train |
Percentage of training set. |
seed |
Random seed for reproducibility. |
List of x.tr, x.te, y.tr, and y.te.
Nan Xiao <https://nanx.me>
Nan Xiao and Qing-Song Xu. (2015). Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection. Journal of Statistical Computation and Simulation 85(18), 3755–3765.
dat <- OHPL.sim( n = 100, p = 100, rho = 0.8, coef = rep(1, 10), snr = 3, p.train = 0.5, seed = 1010 ) dim(dat$x.tr) dim(dat$x.te)
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